# 0. 引入必要的包
import os
import glob
import time
import numpy as np
from tqdm import tqdm
from util import get, preprocess_image, dump
from sklearn.model_selection import train_test_split

# 1. 读取配置文件中的信息
train_dir = get("train") # 获取 训练数据路径
char_styles = get("char_styles") # 获取 字符样式列表，注意: 必须是列标
new_size = get("new_size") # 获取 新图像大小元组, 注意: 必须包含h和w

# 2. 生成X,y

print("# 读取训练数据并进行预处理")
X, y = [], []
for i in char_styles:
    for element in tqdm(glob.glob("../data/shufa/train/train_{}*".format(i)), desc="处理 {} 图像： ".format(i),
                        unit="it/s"):
    #print(f"读取{i}...")
        A = preprocess_image(element, new_size)
        label = os.path.basename(element).split("_")[1]   # 使用`os.path.basename()`函数获取文件名，并使用下划线分割得到标签字符串。
        label_index = char_styles.index(label)
        X.append(A)
        y.append(label_index)
        time.sleep(0.01)
X, y = np.array(X), np.array(y)

#print(X.shape)
# 3. 分割测试集和训练集
print("# 将数据按 80% 和 20% 的比例分割")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#TODO

# 4. 打印样本维度和类型信息
print("X_train: ", X_train.shape, X_train.dtype)  # 训练集特征的维度和类型
print("X_test: ", X_test.shape, X_test.dtype)  # 测试集特征的维度和类型
print("y_train: ", y_train.shape, y_train.dtype)  # 训练集标签的维度和类型
print("y_test: ", y_test.shape, y_test.dtype)  # 测试集标签的维度和类型

# 5. 序列化分割后的训练和测试样本
# TODO
dump((X_train,X_test,y_train,y_test),"(X_train,X_test,y_train,y_test)","{}/Xy".format(get("Xy_root")))
